In 2013 McKinsey predicted a massive talent gap in the analytics and big data space. By 2018, they estimate the shortage will reach some 140,000-190,000 individuals with quantitative skill sets, and 1.5 million analytics-savvy management professionals. It’s apparent that today’s analytics experts are wise to strive for management and leadership positions, because there will probably be no shortage of demand.

Why Analytical Leadership Matters

Is there a need for analytics leadership in the future? Absolutely. While only 4% of organizations are performing sophisticated, predictive analytics, these companies have 30% higher stock market returns and 2.5 times healthier leadership pipelines. Deloitte reports that some 96% of professionals believe analytics will become increasingly important to their organizations in the months to come.

In this blog, you’ll learn some of the skills needed to take your career from analyst or junior data scientist to Chief Analytics Officer (CAO). The skills mentioned here are curated from existing job postings for C-Level analytics professionals, as well as the professional backgrounds of newly-appointed leadership at Hospital Corporation of America (HCA), the White House, and other highly-visible organizations. This isn’t meant to be a comprehensive roadmap, but rather a first step towards developing an understanding of what it may mean to be a Chief Data Officer in the years to come:

1. Well-Established Thought Leadership

Aspiring analytical leaders cannot afford to limit their achievements to in-house projects. Newly-appointed White House Chief Data Scientist DJ Patil has long been an influential voice in the data science community, and has an incredibly impressive list of influential articles and books under his belt.

Individuals aspiring to high-profile leadership roles should start establishing their position as thought leaders as soon as possible through both academic and non-academic publications, conference presentations, and active social media usage. While you may not immediately win an O’Reilly book deal or a coveted spot on the cover of HBR, each contribution to the domain of data science will enhance your personal brand.

2. Tool-Agnostic Data Wrangling

Today’s data scientists can’t afford to take a tools-specific approach to solving big data problems. Many leading data science teams are self-described “tool-agnostic” shops, who may bring together individuals with a broad variety of technical backgrounds. While it’s absolutely critical to have a solid groundwork in Python, Hadoop and related tools; you can’t afford to develop a basic skill set and stop there.

A recent job posting for a Chief Data Scientist at financial giant Capital One requested an individual who can act as a “wrangler.” The job posting specified this probably meant some nine languages and ecosystems, but the point is clear. You can’t afford to be limited by your technical capabilities. Develop the competency to operate seamlessly across many tools and platforms, and you’ll never be limited by what you can’t do.

3. Aggressive, Self-Directed Professional Development

Very few of today’s Chief Data Scientists have graduate coursework in Analytics. In fact, many of the first graduates of Data Science programs are just now entering the workforce. Today’s leaders and highly visible Scientists have backgrounds that may range from Machine Learning to Computer Science, with a few Mathematics, Statistics, and Engineering specialties thrown in. Regardless of how relevant or irrelevant your professional education is, it’s positively critical to have a commitment to self-directed professional development.

A recent posting from Chartbeat specified a candidate with a “strong knowledge of the media space and research topics” and “interest in growing as an engineer.” In fast-changing economic and analytics environments, it’s critical to stay on top of business context, tools, languages, and best practices. Demonstrating a commitment to growing yourself as a professional, regardless of your organization’s support or requirements, will aid you in being appointed to executive leadership roles. Chief Analytics Officers are inherently educators, with a responsibility of informing the enterprise and leading best-of-class data science practices. There’s simply no room for anyone who isn’t continually pursuing personal improvement.

4. Social Selling

The first – and in many cases, even second and third – wave of Chief Analytics Officers may not be welcomed wholeheartedly by every member of the executive leadership teams at their enterprises. While the potential benefit of predictive analytics is clear to most CIOs and CMOs, gaining support and funding for new initiatives from CFOs and others could be a surprising requirement for their role that wasn’t necessarily listed in the job posting. Depending on the sophistication-level of their colleagues and the enterprise, Chief Analytics Officers could find themselves responsible for not only education on the value of analytics, but also internal marketing initiatives and social selling to convince others of the value of the analytics organization.

Edmund Jackson, appointed Chief Data Scientist at HCA in February 2014, has taken a highly visible role in both his organization and the greater Nashville, Tennessee community as one of the first C-Level analytics professionals in his region. For Jackson, this means participating in a great deal of guest speaking opportunities and panels to educate local professionals on the value of data science. While tomorrow’s leaders can hope they won’t have to launch internal campaigns to convince their colleagues of the value of data science, gaining experience with social selling theory and community education can only help your candidacy.

5. Mentorship and Talent Management

While it may be difficult to predict the tools and types of big data problems tomorrow’s analytical leaders could face in their organizations, one thing can safely be predicted. It’s almost certain that a talent gap and shortage of adequately qualified employees will be a theme for many data executives.

Fifth Third Bank’s posting for a Chief Data Officer highlighted many of the soft skills required for successful leadership in a field filled with competition for limited talent. Their ideal candidate will be focused on employee development, recognition and reward, performance feedback, and inspiration. Personnel management and development is likely to be a theme, and today’s sharpest aspiring leaders will gain experience in these arenas, even if they aren’t currently in a management capacity. Mentoring analytical undergraduate students, serving in advisory capacities to career centers, and undergoing voluntary education in human resources topics can only benefit your future.

While these five skills are simply a few of the qualities required to be appointed Chief Analytics of Data Officer at a major organization, they are likely to be major themes as companies increasingly seek high-level analytical leadership. Regardless of where your career path takes you, developing these competencies should have major positive impact on your trajectory.

What are some of the quantitative and interpersonal skills you feel will be critical for aspiring analytical leaders to develop in the years to come?

Keep Calm & Get Hired!

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